Date of Award
Spring 5-2021
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Mahdi Abdelguerfi
Second Advisor
Dr. Tamjid Hoque
Third Advisor
Dr. Elias Ioup
Fourth Advisor
Dr. Shaikh Arifuzzaman
Abstract
Terminal Procedure Charts are a constantly updated and necessary tool for aircraft personnel to approach and take off from airport runways safely. Detecting changes within these charts is a time-consuming and laborious process. Here machine learning techniques were used to predict regions of change in charts based on detecting the charts image regions and comparing features extracted from those regions. Outlined are methodologies to detect differences between two separate charts to produce images with changed regions clearly indicated. Both more conventional computer vision and machine learning techniques were applied. For images with minor shifts, the proposed model is able to ignore them to a greater degree than the baseline.
Recommended Citation
Marchiafava, Anthony M., "Machine Learning for Terminal Procedure Chart Change Detection" (2021). University of New Orleans Theses and Dissertations. 2886.
https://scholarworks.uno.edu/td/2886
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.